Leaf Segmentation in Banana Cultivation Systems: Applications of Photogrammetric Workflow and Remote Sensing Using Multispectral Images from Unmanned Aerial Vehicles
Abstract
This study presents a methodology for leaf segmentation in banana cultivation systems using multispectral images acquired from an unmanned aerial system (UAS), photogrammetric processing, and remote sensing applications within Geographic Information Systems (GIS). The proposed approach integrates the following processes: (1) flight planning and multispectral image acquisition with a multirotor UAS in priority areas of the production system; (2) photogrammetric processing of
RGB and multispectral images; (3) GIS-based analysis of photogrammetric products, such as orthophotos and digital surface models, to support segmentation; (4) segmentation validation; and (5) application of vegetation indices focused exclusively on the segmented leaf structure for targeted monitoring. The results demonstrate effective differentiation of leaf structures in both fragmented and densely cultivated areas. Additionally, the methodology facilitates the application of vegetation
indices to support routine monitoring and serves as a primary criterion for identifying potential foliar
diseases. In this context, the study offers a practical contribution under real operational conditions,
providing guidelines for advancing the technological enhancement of banana production systems. It
focuses on a case study of one of the most significant economic activities in the Colombian Caribbean.
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